Unwarping the Lens: A Physics-Grounded Approach to Video Glasses Removal

High-fidelity removal of eyeglasses from video is a major challenge in facial attribute editing, as the underlying facial geometry is often obscured by complex refractive distortions and view-dependent specular reflections. While large-scale generative priors have shown promise in static image inpainting, they often lack the structural constraints necessary to maintain identity, expression, and pose, leading to visible “identity drift” in both static images and dynamic sequences. In this paper, we propose a novel distillation framework that addresses the stochastic nature of generative priors. Our pipeline first extracts high-fidelity outputs from Gemini, regularizes them via dense geometric landmark constraints to preserve identity, expression, and pose, and finally applies physically-based simulation of lens optics to generate realistic paired training data. This process transfers Gemini’s photo-realistic, multi-view knowledge into a specialized restoration architecture, JFSnet (Joint Feature-Spatial network). JFSnet integrates DINOv2-based semantic features with a convolutional decoder for spatial reconstruction, leveraging equivariance constraints to improve high-frequency detail preservation and stability. Evaluations show that our approach significantly outperforms existing diffusion and GAN-based baselines, achieving the lowest FID scores and ranking highest in user preference studies across visual fidelity, identity preservation, and temporal consistency.

• Machine Perception